**The C#/.NET binding of [llama.cpp](https://github.com/ggerganov/llama.cpp). It provides higher-level APIs to inference the LLaMA Models and deploy it on local device with C#/.NET. It works on
both Windows, Linux and MAC without requirment for compiling llama.cpp yourself. Even without GPU or not enought GPU memory, you can still apply LLaMA models well with this repo. 🤗**
**Furthermore, it provides integrations with other projects such as [semantic-kernel](https://github.com/microsoft/semantic-kernel), [kernel-memory](https://github.com/microsoft/kernel-memory) and [BotSharp](https://github.com/SciSharp/BotSharp) to provide higher-level applications.**
We publish these backends because they are the most popular ones. If none of them matches, please compile the [llama.cpp](https://github.com/ggerganov/llama.cpp) yourself. In this case, please **DO NOT** install the backend packages, instead, add your DLL to your project and ensure it will be copied to the output directory when compiling your project. For more informations please refer to ([this guide](https://scisharp.github.io/LLamaSharp/0.5/ContributingGuide/)).
For [microsoft semantic-kernel](https://github.com/microsoft/semantic-kernel) integration, please search and install the following package:
For [microsoft kernel-memory](https://github.com/microsoft/kernel-memory) integration, please search and install the following package (currently kernel-memory only supports net6.0):
In general, there may be some break changes between two minor releases, for example 0.5.1 and 0.6.0. On the contrary, we don't introduce API break changes in patch release. Therefore it's recommended to keep the highest patch version of a minor release. For example, keep 0.5.6 instead of 0.5.3.
Here's the mapping of them and corresponding model samples provided by `LLamaSharp`. If you're not sure which model is available for a version, please try our sample model.
Many hands make light work. If you have found any other model resource that could work for a version, we'll appreciate it for opening an PR about it! 😊
1. GPU out of memory: Please try setting `n_gpu_layers` to a smaller number.
2. Unsupported model: `llama.cpp` is under quick development and often has break changes. Please check the release date of the model and find a suitable version of LLamaSharp to install, or use the model we provide [on huggingface](https://huggingface.co/AsakusaRinne/LLamaSharpSamples).
3. Cannot find backend package: 1) ensure you installed one of them. 2) check if there's a `libllama.dll` under your output path. 3) check if your system supports avx2, which is the default settings of official runtimes now. If not, please compile llama.cpp yourself.
LLamaSharp provides two ways to run inference: `LLamaExecutor` and `ChatSession`. The chat session is a higher-level wrapping of the executor and the model. Here's a simple example to use chat session.
var prompt = "Transcript of a dialog, where the User interacts with an Assistant named Bob. Bob is helpful, kind, honest, good at writing, and never fails to answer the User's requests immediately and with precision.\r\n\r\nUser: Hello, Bob.\r\nBob: Hello. How may I help you today?\r\nUser: Please tell me the largest city in Europe.\r\nBob: Sure. The largest city in Europe is Moscow, the capital of Russia.\r\nUser:"; // use the "chat-with-bob" prompt here.
The following example shows how to quantize the model. With LLamaSharp you needn't to compile c++ project and run scripts to quantize the model, instead, just run it in C#.
Model in format `gguf` is valid for LLamaSharp (and `ggml` before v0.5.1). One option is to search `LLama` and `gguf` in [huggingface](https://huggingface.co/) to find a model.
Another choice is generate gguf format file yourself with a pytorch weight (or any other), pleae refer to [convert.py](https://github.com/ggerganov/llama.cpp/blob/master/convert.py) and [convert-llama-ggml-to-gguf.py](https://github.com/ggerganov/llama.cpp/blob/master/convert-llama-ggml-to-gguf.py) to get gguf file though a ggml transform.
Any contribution is welcomed! Please read the [contributing guide](https://scisharp.github.io/LLamaSharp/0.4/ContributingGuide/). You can do one of the followings to help us make `LLamaSharp` better:
